An adaptive neuro-fuzzy inference system approach for prediction of power factor in wind turbines
| dc.contributor.author | Raşit ATA | |
| dc.date.accessioned | 2025-04-14T05:53:03Z | |
| dc.date.available | 2025-04-14T05:53:03Z | |
| dc.date.issued | 2009 | |
| dc.description.abstract | This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model for predicting thepower factor of a wind turbine. This model based on the parameters involved for NACA 4415 and LS- 1 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitzcoefficient, end loss, profile type loss, and blade number loss were taken as input variables, while thepower factor was taken as output variable. After a successful learning and training process theproposed model produced reasonable mean errors. The results on a testing data indicate that theANFIS model is found to be more successful than the ANN approach in estimating the power factor. | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14701/55778 | |
| dc.language.iso | İngilizce | |
| dc.subject | Mühendislik | |
| dc.subject | Elektrik ve Elektronik | |
| dc.title | An adaptive neuro-fuzzy inference system approach for prediction of power factor in wind turbines |